International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/3559 2020 2020 PG & Research Department of Mathematics, A.V.V.M Sri Pushpam College (A), Poondi, Thanjavur, Affiliated to Bharathidasan University, Tamil Nadu, India K. K. Department of Mathematics, VTU (RC) CMR Institute of Technology, Bengaluru - 560037, Karnataka, India P. V. N. Hanumantha Ravi Department of Mathematics, VTU (RC) CMR Institute of Technology, Bengaluru - 560037, Karnataka, India K. Meenakshi Department of Mathematics, BMS College of Engineering, Bengaluru – 560019, Karnataka, India S. Shunmugapriya Department of Mathematics, BMS College of Engineering, Bengaluru – 560019, Karnataka, India Shrivalli H.. Y. Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai- 602105, Tamil Nadu, India; Applied Science Research Center, Applied Science Private University, Al-Arab St, Amman -11937, Jordan Elangovan Muniyandy The utilization of neutrosophic fuzzy logic with machine learning constitutes a revolutionary way of improving epidemic modelling. With the help of Weka, this method solves the problem of uncertainty and vagueness that is characteristic of epidemic processes with the help of neutrosophic equations. These equations enhance the way how indeterminacy of epidemic levels can be modelled, therefore enhancing predictions of complex networks. The effectiveness of the proposed framework is confirmed by extensive evaluations providing extensive tables and visualizations regarding the improvements in the accuracy and reliability of the models. Further, the work explores time-optimal control strategies of SIR epidemic models. It shows exactly how bang-bang controls work avoiding the duration of outbreaks drastically, especially if introduced with delayed interventions. This finding is especially important for controlling the health of livestock since the response to disease outbreaks has to be done as soon as possible because of stringent measures on animal health. Altogether, the analysis presented therein contains strong recommendations that would help to improve the handling of epidemics and better understand the approaches to employ in decision-making under conditions of risk and ambiguity. 2025 2025 484 500 10.54216/IJNS.250441 https://www.americaspg.com/articleinfo/21/show/3559